Relevant bibliographies by topics / Deep-Fake detection

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Deep-Fake detection'

Author: Grafiati
Published: 25 May 2024
Last updated: 13 June 2025

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Journal articles on the topic "Deep-Fake detection"

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Doke, Yash. "Deep fake Detection Through Deep Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 861–66. http://dx.doi.org/10.22214/ijraset.2023.51630.

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Abstract: Deep fake is a rapidly growing concern in society, and it has become a significant challenge to detect such manipulated media. Deep fake detection involves identifying whether a media file is authentic or generated using deep learning algorithms. In this project, we propose a deep learning-based approach for detecting deep fakes in videos. We use the Deep fake Detection Challenge dataset, which consists of real and Deep fake videos, to train and evaluate our deep learning model. We employ a Convolutional Neural Network (CNN) architecture for our implementation, which has shown great potential in previous studies. We pre-process the dataset using several techniques such as resizing, normalization, and data augmentation to enhance the quality of the input data. Our proposed model achieves high detection accuracy of 97.5% on the Deep fake Detection Challenge dataset, demonstrating the effectiveness of the proposed approach for deep fake detection. Our approach has the potential to be used in real-world scenarios to detect deep fakes, helping to mitigate the risks posed by deep fakes to individuals and society. The proposed methodology can also be extended to detect in other types of media, such as images and audio, providing a comprehensive solution for deep fake detection.
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K, Mr Gopi. "Deep Fake Detection using Deep Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem33196.

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Deep learning is an effective method that is broadly used across a wide range of areas, i.e., computer vision, machine vision, and natural language processing. Deepfakes is an application of this technology where the images and videos of someone are manipulated in such a way that it is difficult for human beings to tell the difference between them and their true selves. Deepfakes have been the subject of several studies recently, and a number of deep learning approaches have been proposed for their detection. Here, we provide an extensive survey on deepfake generation and recognition techniques using neural networks. Additionally, a detailed study of the different technologies used in deepfake detection is provided. This will surely benefit researchers in this area because it will include new cutting-edge methods for detecting fake videos or images on social networks. Moreover, it will make it easy for us to compare what others have done in their papers by explaining how they came up with their models or what information was employed for training them. Key Words: Deep Learning, Fake Detection, Neural Networks, Social Networks
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D P, Gurukiran. "Deep Fake Detection System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem31014.

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Deep learning methods are used by the Deep Fake Detection System to recognize "deepfakes," or distorted media content. Deepfakes are artificial media produced by sophisticated artificial intelligence algorithms that threaten the credibility of media. The goal of our project is to create a reliable system that can discriminate between authentic and modified content in order to stop the spread of false information and protect media integrity. Our goal is to improve deepfake detection efficiency and accuracy by conducting a thorough evaluation of deep learning-based detection techniques. Our technology aims to offer real-time detection capabilities by utilizing sophisticated neural networks and machine learning techniques. This will aid in the continuous endeavors to tackle the widespread occurrence of deepfakes in digital media. Key Words: Deep Fake Detection, Deep Learning, Media Integrity, Misinformation, Neural Networks
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Harsh Vardhan, Naman Varshney, Manoj Kiran R, Pradeep R, and Dr. Latha N.R. "Deep Fake Video Detection." International Research Journal on Advanced Engineering Hub (IRJAEH) 2, no. 04 (2024): 830–35. http://dx.doi.org/10.47392/irjaeh.2024.0117.

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Deep fake technology, driven by advancements in artificial intelligence, has garnered significant attention in recent years. This paper synthesizes findings from research papers on deep fake technology, focusing on its misuse and the need for further development. The abstracts of selected papers are analyzed to identify trends, methodologies, and challenges in the field. Common themes include the generation, detection, and mitigation of deep fakes, as well as their societal and ethical implications. Through interdisciplinary collaboration, researchers strive to address the risks associated with deep fake misuse while leveraging its potential for positive applications.
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Prof. Dikshendra Sarpate, Abrar Mungi, Shreyash Borkar, Shravani Mane, and Kawnain Shaikh. "A Deep Approach to Deep Fake Detection." International Journal of Scientific Research in Science, Engineering and Technology 11, no. 2 (2024): 530–34. http://dx.doi.org/10.32628/ijsrset2411274.

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In recent months, the proliferation of free deep learning-based software tools has facilitated the creation of credible face exchanges in videos, resulting in what are known as "DeepFake" (DF) videos. While manipulations of digital videos have been demonstrated for several decades through the use of visual effects, recent advances in deep learning have significantly increased the realism of fake content and the accessibility with which it can be created. These AI-synthesized media, popularly referred to as DF, pose a significant challenge for detection. Detecting DF is a major challenge due to the complexity of training algorithms to spot them. In this work, we propose a detection approach using Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). Our system leverages a CNN to extract features at the frame level, which are then used to train an RNN. The RNN learns to classify whether a video has been manipulated, detecting temporal inconsistencies between frames introduced by DF creation tools. We evaluate our approach against a large set of fake videos collected from standard datasets and demonstrate competitive results using a simple architecture.
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A. Sathiya Priya and T. Manisha. "CNN and RNN using Deepfake detection." International Journal of Science and Research Archive 11, no. 2 (2024): 613–18. http://dx.doi.org/10.30574/ijsra.2024.11.2.0460.

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Deep fake Detection is the task of detecting the fake images that have been generated using deep learning techniques. Deep fakes are created by using machine learning algorithms to manipulate or replace parts of an original video or image, such as the face of a person. The goal of deep fake detection is to identify such manipulations and distinguish them from real videos or images. Deep fake technology has emerged as a significant concern in recent years, presenting challenges in various fields, including media authenticity, privacy, and security.
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K. D.V.N.Vaishnavi, L. Hima Bindu, M. Sathvika, K. Udaya Lakshmi, M. Harini, and N. Ashok. "Deep learning approaches for robust deep fake detection." World Journal of Advanced Research and Reviews 21, no. 3 (2023): 2283–89. http://dx.doi.org/10.30574/wjarr.2024.21.3.0889.

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Detecting deepfake images using a deep learning approach, particularly using model Densenet121, involves training a neural network to differentiate between authentic and manipulated images. Deepfakes have gained prominence due to advances in deep learning, especially generative adversarial networks (GANs). They pose significant challenges to the veracity of digital content, as they can be used to create realistic and deceptive media. Deepfakes are realistic looking fake media generated by many artificial intelligence tools like face2face and deepfake, which pose a severe threat to public. As more deepfakes are spreading, we really need better ways to find and prevent them. Deepfake involves creation of highly realistic images and videos and misuse them for spreading fake news, defaming individuals, and possess a significant threat to the integrity of digital content. Our project “Deep Learning Approaches for Robust Deep Fake Detection” aims to address this critical issue by developing a robust system for identification and localization of deep fake content by using ‘Densenet121’ model. This proposed framework seamlessly integrates forgery detection and localization. The dataset used in this project is “140k Real and Fake Faces”, and it consists of 70k real faces from Flickr dataset collected by Nvidia and 70k fake faces sampled from the 1 million Fake faces generated by StyleGAN. For localization purpose, we use GRAD-CAM method to accurately identify the morphed regions. Overall, our goal is to make deepfake detection more effective and reliable in today’s digital landscape.
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Hande, Rutuja, Sneha Goon, Aaditi Gondhali, and Navin Singhaniya. "A Novel Method of Deepfake Detection." ITM Web of Conferences 44 (2022): 03064. http://dx.doi.org/10.1051/itmconf/20224403064.

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Deep-Fake is a novel artificial media technology that uses the likeness of someone else to replace people in existing photographs and films. Deep Learning, as the name implies, is a type of Artificial Intelligence that is used to create it. It is critical to develop counter attacking approaches for detecting fraudulent data. This research examines the Deep-Fake technology in depth. The Deep-Fake Detection discussed here is based on current datasets, such as the Deep-Fake Detection Challenge (DFDC) and Google’s Deep-Fake Detection dataset (DFD). The creation of a bespoke dataset from high-quality Deep-Fakes was utilised to test models. The results of both with and without Transfer Learning were analysed. Finally, the trained models were used to spot well-known deep-fakes of former US President Barack Obama and well-known actor Tom Cruise. A comparison study was performed on all three models. The findings show that the detection are generally domain-specific tasks, however that using Transfer Learning considerably improves the model performance parameters, whereas convolutional RNN gives sequence detection advantage.
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Arunkumar, P. M., Yalamanchili Sangeetha, P. Vishnu Raja, and S. N. Sangeetha. "Deep Learning for Forgery Face Detection Using Fuzzy Fisher Capsule Dual Graph." Information Technology and Control 51, no. 3 (2022): 563–74. http://dx.doi.org/10.5755/j01.itc.51.3.31510.

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In digital manipulation, creating fake images/videos or swapping face images/videos with another person is done by using a deep learning algorithm is termed deep fake. Fake pornography is a harmful one because of the inclusion of fake content in the hoaxes, fake news, and fraud things in the financial. The Deep Learning technique is an effective tool in the detection of deep fake images or videos. With the advancement of Generative adversarial networks (GAN) in the deep learning techniques, deep fake has become an essential one in the social media platform. This may threaten the public, therefore detection of deep fake images/videos is needed. For detecting the forged images/videos, many research works have been done and those methods are inefficient in the detection of new threats or newly created forgery images or videos, and also consumption time is high. Therefore, this paper focused on the detection of different types of fake images or videos using Fuzzy Fisher face with Capsule dual graph (FFF-CDG). The data set used in this work is FFHQ, 100K-Faces DFFD, VGG-Face2, and Wild Deep fake. The accuracy for FFHQ datasets, the existing and proposed systems obtained the accuracy of 81.5%, 89.32%, 91.35%, and 95.82% respectively.
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ST, Suganthi, Mohamed Uvaze Ahamed Ayoobkhan, Krishna Kumar V, et al. "Deep learning model for deep fake face recognition and detection." PeerJ Computer Science 8 (February 22, 2022): e881. http://dx.doi.org/10.7717/peerj-cs.881.

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Deep Learning is an effective technique and used in various fields of natural language processing, computer vision, image processing and machine vision. Deep fakes uses deep learning technique to synthesis and manipulate image of a person in which human beings cannot distinguish the fake one. By using generative adversarial neural networks (GAN) deep fakes are generated which may threaten the public. Detecting deep fake image content plays a vital role. Many research works have been done in detection of deep fakes in image manipulation. The main issues in the existing techniques are inaccurate, consumption time is high. In this work we implement detecting of deep fake face image analysis using deep learning technique of fisherface using Local Binary Pattern Histogram (FF-LBPH). Fisherface algorithm is used to recognize the face by reduction of the dimension in the face space using LBPH. Then apply DBN with RBM for deep fake detection classifier. The public data sets used in this work are FFHQ, 100K-Faces DFFD, CASIA-WebFace.
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Dissertations / Theses on the topic "Deep-Fake detection"

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Tak, Hemlata. "End-to-End Modeling for Speech Spoofing and Deepfake Detection." Electronic Thesis or Diss., Sorbonne université, 2023. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2023SORUS104.pdf.

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Les systèmes biométriques vocaux sont utilisés dans diverses applications pour une authentification sécurisée. Toutefois, ces systèmes sont vulnérables aux attaques par usurpation d'identité. Il est donc nécessaire de disposer de techniques de détection plus robustes. Cette thèse propose de nouvelles techniques de détection fiables et efficaces contre les attaques invisibles. La première contribution est un ensemble non linéaire de classificateurs de sous-bandes utilisant chacun un modèle de mélange gaussien. Des résultats compétitifs montrent que les modèles qui apprennent des indices discriminants spécifiques à la sous-bande peuvent être nettement plus performants que les modèles entraînés sur des signaux à bande complète. Étant donné que les DNN sont plus puissants et peuvent effectuer à la fois l'extraction de caractéristiques et la classification, la deuxième contribution est un modèle RawNet2. Il s'agit d'un modèle de bout en bout qui apprend les caractéristiques directement à partir de la forme d'onde brute. La troisième contribution comprend la première utilisation de réseaux neuronaux graphiques (GNN) avec un mécanisme d'attention pour modéliser la relation complexe entre les indices d'usurpation présents dans les domaines spectral et temporel. Nous proposons un réseau d'attention spectro-temporel E2E appelé RawGAT-ST. Il est ensuite étendu à un réseau d'attention spectro-temporel intégré, appelé AASIST, qui exploite la relation entre les graphes spectraux et temporels hétérogènes. Enfin, cette thèse propose une nouvelle technique d'augmentation des données appelée RawBoost et utilise un modèle vocal auto-supervisé et pré-entraîné pour améliorer la généralisation<br>Voice biometric systems are being used in various applications for secure user authentication using automatic speaker verification technology. However, these systems are vulnerable to spoofing attacks, which have become even more challenging with recent advances in artificial intelligence algorithms. There is hence a need for more robust, and efficient detection techniques. This thesis proposes novel detection algorithms which are designed to perform reliably in the face of the highest-quality attacks. The first contribution is a non-linear ensemble of sub-band classifiers each of which uses a Gaussian mixture model. Competitive results show that models which learn sub-band specific discriminative information can substantially outperform models trained on full-band signals. Given that deep neural networks are more powerful and can perform both feature extraction and classification, the second contribution is a RawNet2 model. It is an end-to-end (E2E) model which learns features directly from raw waveform. The third contribution includes the first use of graph neural networks (GNNs) with an attention mechanism to model the complex relationship between spoofing cues present in spectral and temporal domains. We propose an E2E spectro-temporal graph attention network called RawGAT-ST. RawGAT-ST model is further extended to an integrated spectro-temporal graph attention network, named AASIST which exploits the relationship between heterogeneous spectral and temporal graphs. Finally, this thesis proposes a novel data augmentation technique called RawBoost and uses a self-supervised, pre-trained speech model as a front-end to improve generalisation in the wild conditions
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Falade, Joannes Chiderlos. "Identification rapide d'empreintes digitales, robuste à la dissimulation d'identité." Thesis, Normandie, 2020. http://www.theses.fr/2020NORMC231.

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La biométrie est de plus en plus utilisée à des fins d’identification compte tenu de la relation étroite entre la personne et son identifiant (comme une empreinte digitale). Nous positionnons cette thèse sur la problématique de l’identification d’individus à partir de ses empreintes digitales. L’empreinte digitale est une donnée biométrique largement utilisée pour son efficacité, sa simplicité et son coût d’acquisition modeste. Les algorithmes de comparaison d’empreintes digitales sont matures et permettent d’obtenir en moins de 500 ms un score de similarité entre un gabarit de référence (stocké sur un passeport électronique ou une base de données) et un gabarit acquis. Cependant, il devient très important de déterminer l'identité d'un individu contre une population entière en un temps très court (quelques secondes). Ceci représente un enjeu important compte tenu de la taille de la base de données biométriques (contenant un ensemble d’individus de l’ordre d’un pays). Par exemple, avant de délivrer un nouveau passeport à un individu qui en fait la demande, il faut faire une recherche d'identification sur la base des données biométriques du pays afin de s'assurer que ce dernier n'en possède pas déjà un autre mais avec les mêmes empreintes digitales (éviter les doublons). Ainsi, la première partie du sujet de cette thèse concerne l’identification des individus en utilisant les empreintes digitales. D’une façon générale, les systèmes biométriques ont pour rôle d’assurer les tâches de vérification (comparaison 1-1) et d’identification (1-N). Notre sujet se concentre sur l’identification avec N étant à l’échelle du million et représentant la population d’un pays par exemple. Dans le cadre de nos travaux, nous avons fait un état de l’art sur les méthodes d’indexation et de classification des bases de données d’empreintes digitales. Nous avons privilégié les représentations binaires des empreintes digitales pour indexation. Tout d’abord, nous avons réalisé une étude bibliographique et rédigé un support sur l’état de l’art des techniques d’indexation pour la classification des empreintes digitales. Ensuite, nous avons explorer les différentes représentations des empreintes digitales, puis réaliser une prise en main et l’évaluation des outils disponibles à l’imprimerie Nationale (IN Groupe) servant à l'extraction des descripteurs représentant une empreinte digitale. En partant de ces outils de l’IN, nous avons implémenté quatre méthodes d’identification sélectionnées dans l’état de l’art. Une étude comparative ainsi que des améliorations ont été proposées sur ces méthodes. Nous avons aussi proposé une nouvelle solution d'indexation d'empreinte digitale pour réaliser la tâche d’identification qui améliore les résultats existant. Les différents résultats sont validés sur des bases de données de tailles moyennes publiques et nous utilisons le logiciel Sfinge pour réaliser le passage à l’échelle et la validation complète des stratégies d’indexation. Un deuxième aspect de cette thèse concerne la sécurité. Une personne peut avoir en effet, la volonté de dissimuler son identité et donc de mettre tout en œuvre pour faire échouer l’identification. Dans cette optique, un individu peut fournir une empreinte de mauvaise qualité (portion de l’empreinte digitale, faible contraste en appuyant peu sur le capteur…) ou fournir une empreinte digitale altérée (empreinte volontairement abîmée, suppression de l’empreinte avec de l’acide, scarification…). Il s'agit donc dans la deuxième partie de cette thèse de détecter les doigts morts et les faux doigts (silicone, impression 3D, empreinte latente) utilisés par des personnes mal intentionnées pour attaquer le système. Nous avons proposé une nouvelle solution de détection d'attaque basée sur l'utilisation de descripteurs statistiques sur l'empreinte digitale. Aussi, nous avons aussi mis en place trois chaînes de détections des faux doigts utilisant les techniques d'apprentissages profonds<br>Biometrics are increasingly used for identification purposes due to the close relationship between the person and their identifier (such as fingerprint). We focus this thesis on the issue of identifying individuals from their fingerprints. The fingerprint is a biometric data widely used for its efficiency, simplicity and low cost of acquisition. The fingerprint comparison algorithms are mature and it is possible to obtain in less than 500 ms a similarity score between a reference template (enrolled on an electronic passport or database) and an acquired template. However, it becomes very important to check the identity of an individual against an entire population in a very short time (a few seconds). This is an important issue due to the size of the biometric database (containing a set of individuals of the order of a country). Thus, the first part of the subject of this thesis concerns the identification of individuals using fingerprints. Our topic focuses on the identification with N being at the scale of a million and representing the population of a country for example. Then, we use classification and indexing methods to structure the biometric database and speed up the identification process. We have implemented four identification methods selected from the state of the art. A comparative study and improvements were proposed on these methods. We also proposed a new fingerprint indexing solution to perform the identification task which improves existing results. A second aspect of this thesis concerns security. A person may want to conceal their identity and therefore do everything possible to defeat the identification. With this in mind, an individual may provide a poor quality fingerprint (fingerprint portion, low contrast by lightly pressing the sensor...) or provide an altered fingerprint (impression intentionally damaged, removal of the impression with acid, scarification...). It is therefore in the second part of this thesis to detect dead fingers and spoof fingers (silicone, 3D fingerprint, latent fingerprint) used by malicious people to attack the system. In general, these methods use machine learning techniques and deep learning. Secondly, we proposed a new presentation attack detection solution based on the use of statistical descriptors on the fingerprint. Thirdly, we have also build three presentation attacks detection workflow for fake fingerprint using deep learning. Among these three deep solutions implemented, two come from the state of the art; then the third an improvement that we propose. Our solutions are tested on the LivDet competition databases for presentation attack detection
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Mohawesh, RIM. "Machine learning approaches for fake online reviews detection." Thesis, 2022. https://eprints.utas.edu.au/47578/.

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Online reviews have a substantial impact on decision making in various areas of society, predominantly in the arena of buying and selling of goods. The truthfulness of online reviews is critical for both consumers and vendors. Genuine reviews can lead to satisfied customers and success for quality businesses, whereas fake reviews can mislead innocent clients, influence customers’ choices owing to false descriptions and inaccurate sales. Therefore, there is a need for efficient fake review detection models and tools that can help distinguish between fraudulent and legitimate reviews to protect the ecosystem of e-commerce sites, consumers, and companies from these misleading fake reviews. Although several fake review detection models have been proposed in the literature, there are still some challenges regarding the performance of these models that need to be addressed. For instance, existing studies are highly dependent on linguistic features, but these are not enough to capture the semantic meaning of the reviews, necessary for improving prediction performance. Furthermore, when analysing a fake review text data stream, concept drift may occur where the input and output relationship of text changes over time, affecting model performance. The concept drift problem and its performance impact on fake review detection models has yet to be addressed. Moreover, existing models have only focused upon the review text, reviewer-based features, or both. They have also used a limited number of behavioural and content features that reduce the accuracy of the detection model. Further, these models have used fine-grained features (e.g., words) or coarse-grained features (e.g., documents, sentences, topics) separately, reducing the detection models’ performance. To address the above discussed research gaps, this research investigates and analyses the performance of different neural network models and advanced pre-trained models for fake review detection. Then, we propose an ensemble model that combines three transformer models to detect fake reviews on semi-real-world datasets. This research also investigates the concept drift problem by detecting the occurrence of concept drift within text data streams of fake reviews, finding a correlation between concept drift (if it exists) and the performance of detection models over time in the real-world data stream. Furthermore, this research proposes a new Interpretable ensemble of multi-view deep learning model (EEMVDLM) that can detect fake reviews based on different feature perspectives and classifiers and provide interpretability of deep learning models. This ensemble model comprises three popular machine learning models: bidirectional long-short-term-memory (Bi-LSTM), convolutional neural network (CNN), and deep neural network (DNN). Additionally, this model provides interpretations to achieve reliable results from deep learning models, which are usually considered as "Black Boxes". For this purpose, the shapley additive explanations (SHAP) method and attention mechanism are used to understand the underlying logic of a model and provide other hints to determine whether it is "unfair". In summary, this research provides the following contributions: (1) Comprehensive survey that analyses the task of fake review detection by providing the existing approaches, existing feature extraction techniques, challenges, and available datasets. (2) This research investigates and analyses the performance of different neural network models and transformers to demonstrate their effect on fake review detection. Experimental results show that the transformer models perform well with a small dataset for fake review detection. Specifically, the robustly optimised BERT pretraining approach (RoBERTa) achieves the highest accuracy. (3) This research proposes an ensemble of three transformer models to discover the hidden patterns in a sequence of fake reviews and detect them precisely. Experimental results on two semi-real datasets show that the proposed model outperforms the state-of-the-art methods. (4) This research provides an in-depth analysis for detecting concept drift within fake review data streams by using two methods: benchmarking concept drift detection methods and contentbased classification methods. The results demonstrate that there is a strong negative correlation between concept drift and the performance of fake review detection/prediction models, which indicates the difficulty of building more efficient models. (5) This research proposes a new Interpretable ensemble of multi-view deep learning model (EEMVDLM) that can detect fake reviews based on different feature perspectives and classifiers. The experimental results on two real-life datasets present excellent performance and outperformed the state-of-the-art methods. Further, the experimental results prove that our proposed model can provide reasonable interpretations that help users understand why certain reviews are classified as fake or genuine. To the best of our knowledge, this research provides the first study that incorporates advanced pre-trained models, investigates the concept drift problem, and the first Interpretable fake review detection approach. The findings of this thesis contribute to both practical and theoretical applications.
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Palanisamy, Sundar Agnideven. "Learning-based Attack and Defense on Recommender Systems." Thesis, 2021. http://dx.doi.org/10.7912/C2/65.

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Indiana University-Purdue University Indianapolis (IUPUI)<br>The internet is the home for massive volumes of valuable data constantly being created, making it difficult for users to find information relevant to them. In recent times, online users have been relying on the recommendations made by websites to narrow down the options. Online reviews have also become an increasingly important factor in the final choice of a customer. Unfortunately, attackers have found ways to manipulate both reviews and recommendations to mislead users. A Recommendation System is a special type of information filtering system adapted by online vendors to provide suggestions to their customers based on their requirements. Collaborative filtering is one of the most widely used recommendation systems; unfortunately, it is prone to shilling/profile injection attacks. Such attacks alter the recommendation process to promote or demote a particular product. On the other hand, many spammers write deceptive reviews to change the credibility of a product/service. This work aims to address these issues by treating the review manipulation and shilling attack scenarios independently. For the shilling attacks, we build an efficient Reinforcement Learning-based shilling attack method. This method reduces the uncertainty associated with the item selection process and finds the most optimal items to enhance attack reach while treating the recommender system as a black box. Such practical online attacks open new avenues for research in building more robust recommender systems. When it comes to review manipulations, we introduce a method to use a deep structure embedding approach that preserves highly nonlinear structural information and the dynamic aspects of user reviews to identify and cluster the spam users. It is worth mentioning that, in the experiment with real datasets, our method captures about 92\% of all spam reviewers using an unsupervised learning approach.
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(11190282), Agnideven Palanisamy Sundar. "Learning-based Attack and Defense on Recommender Systems." Thesis, 2021.

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The internet is the home for massive volumes of valuable data constantly being created, making it difficult for users to find information relevant to them. In recent times, online users have been relying on the recommendations made by websites to narrow down the options. Online reviews have also become an increasingly important factor in the final choice of a customer. Unfortunately, attackers have found ways to manipulate both reviews and recommendations to mislead users. A Recommendation System is a special type of information filtering system adapted by online vendors to provide suggestions to their customers based on their requirements. Collaborative filtering is one of the most widely used recommendation systems; unfortunately, it is prone to shilling/profile injection attacks. Such attacks alter the recommendation process to promote or demote a particular product. On the other hand, many spammers write deceptive reviews to change the credibility of a product/service. This work aims to address these issues by treating the review manipulation and shilling attack scenarios independently. For the shilling attacks, we build an efficient Reinforcement Learning-based shilling attack method. This method reduces the uncertainty associated with the item selection process and finds the most optimal items to enhance attack reach while treating the recommender system as a black box. Such practical online attacks open new avenues for research in building more robust recommender systems. When it comes to review manipulations, we introduce a method to use a deep structure embedding approach that preserves highly nonlinear structural information and the dynamic aspects of user reviews to identify and cluster the spam users. It is worth mentioning that, in the experiment with real datasets, our method captures about 92\% of all spam reviewers using an unsupervised learning approach.<br>
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CHIANG, YAN-MENG, and 江彥孟. "An empirical study on detecting fake reviews using deep learning and machine learning techniques." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/7b939e.

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碩士<br>東吳大學<br>資訊管理學系<br>106<br>The increasing share of the online businesses in market economy has led to a larger influence and importance of the online reviews. Before making a purchase, users are increasingly inclined to browse online forum that are posted to share post-purchase experiences of products and services. However, there are many fake reviews in the real world, consumers can't identify authentic and fake reviews. Fake online shopping reviews are harmful to consumers who might buy misrepresented products. Therefore, we proposed a framework which could detect fake reviews. In this study, we focused on the data on the web forum called Mobile01 and used text mining to deal with textual data including Bag-of-words, Latent Semantic Analysis and word2vec for word representation. Next, we used machine learning to train the model to detect fake review, including SVM, Deep Neural Network (DNN), Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). Finally, we chose three best performance models to vote and hope that these fake reviews samples can be the reference in the future research.
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Book chapters on the topic "Deep-Fake detection"

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Chambial, Shourya, Rishabh Budhia, Tanisha Pandey, B. K. Tripathy, and A. Tripathy. "Deep Fake Generation and Detection." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1203-2_45.

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G, Santhosh Kumar. "Deep Learning for Fake News Detection." In Data Science for Fake News. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62696-9_4.

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Sharma, Srishti, Mala Saraswat, and Anil Kumar Dubey. "Fake News Detection Using Deep Learning." In Knowledge Graphs and Semantic Web. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-91305-2_19.

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Avram, Camelia, George Mesaroş, and Adina Aştilean. "Deep Neural Networks in Fake News Detection." In Innovations in Mechatronics Engineering II. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09385-2_3.

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Masciari, Elio, Vincenzo Moscato, Antonio Picariello, and Giancarlo Sperli. "A Deep Learning Approach to Fake News Detection." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59491-6_11.

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Pimple, Kanchan M., Ravindra R. Solanke, Praveen P. Likhitkar, and Sagar Pande. "Fake Video News Detection Using Deep Learning Algorithm." In Proceedings of Third Doctoral Symposium on Computational Intelligence. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3148-2_72.

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Singh, Aadya, Abey Alex George, Pankaj Gupta, and Lakshmi Gadhikar. "ShallowFake-Detection of Fake Videos Using Deep Learning." In Conference Proceedings of ICDLAIR2019. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67187-7_19.

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Sharma, Vaibhav, Divya Pratap Singh, Jatin Rana, Anjali Kapoor, and Anju Mishra. "Deep Learning Model for Indian Fake Currency Detection." In Algorithms for Intelligent Systems. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-8398-8_8.

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Monisha, G. B., and Jyothi S. Nayak. "Detection of Online Fake Review Using Deep Learning." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-1329-5_13.

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Singhania, Sneha, Nigel Fernandez, and Shrisha Rao. "3HAN: A Deep Neural Network for Fake News Detection." In Neural Information Processing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70096-0_59.

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Conference papers on the topic "Deep-Fake detection"

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Bide, Pramod, Varun, Gaurav Patil, Samveg Shah, and Sakshi Patil. "Fakequipo: Deep Fake Detection." In 2022 IEEE 3rd Global Conference for Advancement in Technology (GCAT). IEEE, 2022. http://dx.doi.org/10.1109/gcat55367.2022.9972149.

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Aggarwal, Aakriti, Siddhant Wadhwa, Pallav Gupta, Nishit Anand, and Rashmi Kushwah. "IsSwap: Deep Fake Detection." In 2021 7th International Conference on Signal Processing and Communication (ICSC). IEEE, 2021. http://dx.doi.org/10.1109/icsc53193.2021.9673225.

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Gupta, Diksha, Shruti Mishra, Meenu Gupta, and Rakesh Kumar. "Deep Fake detection using deep learning." In INTERNATIONAL CONFERENCE ON INTELLIGENT AND SMART COMPUTATION (ICIASC-2023). AIP Publishing, 2024. http://dx.doi.org/10.1063/5.0198661.

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Nguyen, Duc Minh, Tien Huu Do, Robert Calderbank, and Nikos Deligiannis. "Fake News Detection using Deep." In Proceedings of the 2019 Conference of the North. Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/n19-1141.

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Kanchana, M., Vel Murugesh Kumar, T. P. Anish., and Pv Gopirajan. "Deep Fake BERT: Efficient Online Fake News Detection System." In 2023 International Conference on Networking and Communications (ICNWC). IEEE, 2023. http://dx.doi.org/10.1109/icnwc57852.2023.10127560.

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Thennavan, Swetha, Divya Gokul, and Avila Jayapalan. "Deep Learning Based Fake Stamp Detection." In 2023 International Conference on Computer Communication and Informatics (ICCCI). IEEE, 2023. http://dx.doi.org/10.1109/iccci56745.2023.10128600.

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Kong, Sheng How, Li Mei Tan, Keng Hoon Gan, and Nur Hana Samsudin. "Fake News Detection using Deep Learning." In 2020 IEEE 10th Symposium on Computer Applications & Industrial Electronics (ISCAIE). IEEE, 2020. http://dx.doi.org/10.1109/iscaie47305.2020.9108841.

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Singh, Yuvraj, Chetanya Arora, Nishant Kumar Lakda, Kartik Tyagi, and Deeksha Kumari. "Fake News Detection Using Deep Learning." In 2023 6th International Conference on Contemporary Computing and Informatics (IC3I). IEEE, 2023. http://dx.doi.org/10.1109/ic3i59117.2023.10397922.

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S, Kingsley, Vasanth Kumar A, Santhosh Prabhu P, and Parthiban M. "Deep Fake Detection using Advance ConvNets2D." In 2023 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS). IEEE, 2023. http://dx.doi.org/10.1109/icscds56580.2023.10104847.

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Adnan, Sarah Riyadh, and Huda Abdulaali Abdulbaqi. "Investigation of deep fake video detection." In THE FOURTH AL-NOOR INTERNATIONAL CONFERENCE FOR SCIENCE AND TECHNOLOGY (4NICST2022). AIP Publishing, 2024. http://dx.doi.org/10.1063/5.0202454.

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Reports on the topic "Deep-Fake detection"

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Wachs, Brandon. Satellite Image Deep Fake Creation and Detection. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1812627.

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